Video Clip 5

2013 ◽  
pp. 755-756
Keyword(s):  
Video Clip

Representasi Feminisme dalam Video Klip ‘Nightmare’ oleh Halsey

Koneksi ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 338
Author(s):  
Faiz Zulia Maharany ◽  
Ahmad Junaidi
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Data Collection ◽  
Mass Communication ◽  
Video Clip ◽  
Charles Sanders Peirce ◽  
Patriarchal Culture ◽  
The Social ◽  
Social Media Platforms ◽  
Barrier Wall

'Nightmare' is the title of a video clip belonging to a singer and singer called Halsey, in which the video clip is explained about the figure of women who struggle against patriarchal culture which has been a barrier wall for women to get their rights, welfare and the equality needed they get. This research uses descriptive qualitative research methods. Data collection techniques are done through documentation, observation and study of literature. Then, analyzed using Charles Sanders Peirce's semiotics technique. The results of this study show the fact that signs, symbols or messages representing feminism in the video, 'Nightmare' clips are presented through scenes that present women's actions in opposing domination over men and sarcastic sentences contained in the lyrics of the song to discuss with patriarchy. Youtube as one of the social media platforms where the 'Nightmare' video clip is uploaded is very effective for mass communication and for conveying the message contained in the video clip to the viewing public.‘Nightmare’ adalah judul video klip milik musisi sekaligus penyanyi yang bernama Halsey, dimana pada Video klipnya tersebut menceritakan tentang figur perempuan-perempuan yang berusaha melawan budaya patriarki yang selama ini telah menjadi dinding penghalang bagi perempuan untuk mendapatkan hak-haknya, keadilan dan kesetaraan yang seharusnya mereka dapatkan. Penelitian ini menggunakan metode penelitian kualitatif deskriptif. Teknik pengumpulan data dilakukan melalui dokumentasi, observasi dan studi kepustakaan. Kemudian, dianalisis menggunakan teknik semiotika milik Charles Sanders Peirce. Hasil penelitian ini menunjukan bahwa terdapat tanda-tanda, simbol atau pesan yang merepresentasikan feminisme di dalam video klip ‘Nightmare’ yang dihadirkan melalui adegan-adegan yang menyajikan aksi perempuan dalam menolak dominasi atas laki-laki dan kalimat-kalimat sarkas yang terkandung dalam lirik lagunya untuk ditujukan kepada patriarki. Youtube sebagai salah satu platform media sosial dimana video klip ‘Nightmare’ diunggah sangat efektif untuk melakukan komunikasi massa dan untuk menyampaikan pesan yang terkandung di dalam video klip tersebut kepada masyarakat yang menonton.

Ana Tijoux y el movimiento estudiantil chileno del 2011

2019 ◽  
pp. 124-132
Author(s):  
María José Barros Cruz
Keyword(s):  
Video Clip

En el artículo se realiza un análisis de canción “Shock” de la rapera y activista chilena Ana Tijoux, compuesta en el marco de las movilizaciones estudiantiles del año 2011 en Chile. Proponemos entender este trabajo musical como parte de un movimiento social que salió a las calles para decir su indignación frente al sistema educacional de corte neoliberal impuesto durante la dictadura de Pinochet y prolongado durante la transición democrática. Atenta tanto a las voces de la calle como a los saberes letrados, en esta canción Tijoux realiza una fuerte crítica a la clase política del Chile (post)dictatorial, reivindicando la alianza entre los cuerpos y la ocupación del espacio público (Butler 2017) por parte de los estudiantes y ciudadanos movilizados. Para ello elabora a una retórica de lo colectivo manifiesta en la letra y el video clip y recurre a un género musical masivo y popular como el rap.

scholarly journals Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average

2021 ◽  
Vol 11 (12) ◽  
pp. 5563
Author(s):  
Jinsol Ha ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Action Recognition ◽  
High Speed ◽  
Moving Average ◽  
Video Clip ◽  
Visual Surveillance ◽  
Temporal Information ◽  
Feature Maps ◽  
Short Term ◽  
Accurate Analysis ◽  
Difference Images

Action recognition requires the accurate analysis of action elements in the form of a video clip and a properly ordered sequence of the elements. To solve the two sub-problems, it is necessary to learn both spatio-temporal information and the temporal relationship between different action elements. Existing convolutional neural network (CNN)-based action recognition methods have focused on learning only spatial or temporal information without considering the temporal relation between action elements. In this paper, we create short-term pixel-difference images from the input video, and take the difference images as an input to a bidirectional exponential moving average sub-network to analyze the action elements and their temporal relations. The proposed method consists of: (i) generation of RGB and differential images, (ii) extraction of deep feature maps using an image classification sub-network, (iii) weight assignment to extracted feature maps using a bidirectional, exponential, moving average sub-network, and (iv) late fusion with a three-dimensional convolutional (C3D) sub-network to improve the accuracy of action recognition. Experimental results show that the proposed method achieves a higher performance level than existing baseline methods. In addition, the proposed action recognition network takes only 0.075 seconds per action class, which guarantees various high-speed or real-time applications, such as abnormal action classification, human–computer interaction, and intelligent visual surveillance.

scholarly journals Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow

2020 ◽  
Vol 34 (07) ◽  
pp. 10713-10720
Author(s):  
Mingyu Ding ◽  
Zhe Wang ◽  
Bolei Zhou ◽  
Jianping Shi ◽  
Zhiwu Lu ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Segmentation ◽  
Video Clip ◽  
Semantic Segmentation ◽  
Temporal Consistency ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Optical Flows ◽  
Benchmark Datasets ◽  
Spatio Temporal

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.

Do They Really Mean It? Children’s Inference of Speaker Intentions and the Role of Age and Gender

2019 ◽  
Vol 63 (4) ◽  
pp. 689-712
Author(s):  
K. Rothermich ◽  
O. Caivano ◽  
L.J. Knoll ◽  
V. Talwar
Keyword(s):  
Gender Differences ◽  
Young Adults ◽  
Video Clip ◽  
Age And Gender ◽  
Video Clips ◽  
Age And Gender Differences ◽  
Speaker Intentions ◽  
And Gender

Interpreting other people’s intentions during communication represents a remarkable challenge for children. Although many studies have examined children’s understanding of, for example, sarcasm, less is known about their interpretation. Using realistic audiovisual scenes, we invited 124 children between 8 and 12 years old to watch video clips of young adults using different speaker intentions. After watching each video clip, children answered questions about the characters and their beliefs, and the perceived friendliness of the speaker. Children’s responses reveal age and gender differences in the ability to interpret speaker belief and social intentions, especially for scenarios conveying teasing and prosocial lies. We found that the ability to infer speaker belief of prosocial lies and to interpret social intentions increases with age. Our results suggest that children at the age of 8 years already show adult-like abilities to understand literal statements, whereas the ability to infer specific social intentions, such as teasing and prosocial lies, is still developing between the age of 8 and 12 years. Moreover, girls performed better in classifying prosocial lies and sarcasm as insincere than boys. The outcomes expand our understanding of how children observe speaker intentions and suggest further research into the development of teasing and prosocial lie interpretation.

The treatment of magical ideation in two individuals with obsessive compulsive disorder

2010 ◽  
Vol 4 (1) ◽  
pp. 16-29 ◽  
Author(s):  
Danielle A. Einstein ◽  
Ross G. Menzies ◽  
Tamsen St Clare ◽  
Juliette Drobny ◽  
Fjola Dogg Helgadottir
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Video Clip ◽  
Magical Thinking ◽  
Behaviour Therapy ◽  
Obsessive Compulsive ◽  
Magical Ideation ◽  
Obsessive Compulsive Symptoms ◽  
Compulsive Disorder ◽  
Mediating Role ◽  
Pre Treatment

AbstractData collected from clinical populations indicate that magical ideation (MI) may play a causal or a mediating role in the expression of obsessive compulsive symptoms. If this is the case then when targeted in treatment, symptoms of obsessive compulsive disorder (OCD) should be altered. Two individuals diagnosed with OCD received a trial treatment targeting magical thinking. The intervention consisted of a series of procedures designed to undermine superstitious/MI without targeting obsessions or compulsions. The procedures involved critical analysis of the following material: (1) a free astrology offer; (2) a horoscope prediction exercise; (3) a description of four different cultural explanations of the origin of fire; (4) an instructive guide for Tarot card readers; (5) a report of a UFO sighting; (6) a video-clip describing a cult festival; (7) a description of a ‘hoax’ channeler and (8) a superstition exercise. Measures of obsessive compulsive symptoms, superstition, MI and thought–action fusion were administered pre-treatment, post-treatment and at 3 months’ follow-up. According to the twofold criterion of Jacobson et al. (Behaviour Therapy 1984, 15, 336–352), following treatment the patients were identified as being recovered on measures of magical and superstitious thinking and on the Padua Inventory.

scholarly journals Instance Sequence Queries for Video Instance Segmentation with Transformers

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4507
Author(s):  
Zhujun Xu ◽  
Damien Vivet
Keyword(s):  
Data Association ◽  
Video Clip ◽  
Complex Data ◽  
Training Procedure ◽  
Post Processing ◽  
Bipartite Matching ◽  
Frame Method ◽  
Memory Constraints ◽  
Instance Segmentation

Existing methods for video instance segmentation (VIS) mostly rely on two strategies: (1) building a sophisticated post-processing to associate frame level segmentation results and (2) modeling a video clip as a 3D spatial-temporal volume with a limit of resolution and length due to memory constraints. In this work, we propose a frame-to-frame method built upon transformers. We use a set of queries, called instance sequence queries (ISQs), to drive the transformer decoder and produce results at each frame. Each query represents one instance in a video clip. By extending the bipartite matching loss to two frames, our training procedure enables the decoder to adjust the ISQs during inference. The consistency of instances is preserved by the corresponding order between query slots and network outputs. As a result, there is no need for complex data association. On TITAN Xp GPU, our method achieves a competitive 34.4% mAP at 33.5 FPS with ResNet-50 and 35.5% mAP at 26.6 FPS with ResNet-101 on the Youtube-VIS dataset.

scholarly journals Influence of a Dissection Video Clip on Anxiety, Affect, and Self-Efficacy in Educational Dissection: A Treatment Study

2016 ◽  
Vol 15 (1) ◽  
pp. ar1 ◽  
Author(s):  
Christoph Randler ◽  
Eda Demirhan ◽  
Peter Wüst-Ackermann ◽  
Inga H. Desch
Keyword(s):  
Life History ◽  
Negative Affect ◽  
Self Efficacy ◽  
Negative Emotions ◽  
Video Clip ◽  
Biology Teachers ◽  
Treatment Comparison ◽  
Film Treatment ◽  
Efficacy Measure ◽  
The Cost

In science education, dissections of animals are an integral part of teaching, but they often evoke negative emotions. We aimed at reducing negative emotions (anxiety, negative affect [NA]) and increasing positive affect (PA) and self-efficacy by an experimental intervention using a predissection video to instruct students about fish dissection. We compared this treatment with another group that watched a life history video about the fish. The participants were 135 students studying to become biology teachers. Seventy received the treatment with the dissection video, and 65 viewed the life history video. We applied a pre/posttest treatment-comparison design and used the Positive and Negative Affect Schedule (PANAS), the State–Trait–Anxiety Inventory for State (STAI-S), and a self-efficacy measure three times: before the lesson (pretest), after the film treatment (posttest 1), and after the dissection (posttest 2). The dissection film group scored higher in PA, NA, and state anxiety (STAI-S) after the dissection video treatment and higher in self-efficacy after the dissection. The life history group showed no differences between the pretest and posttest 1. The dissection film has clear benefits—increasing PA and self-efficacy—that come at the cost of higher NA and higher STAI-S.

The STRokE DOC Trial Technique: ‘Video Clip, Drip, and/or Ship’

2007 ◽  
Vol 2 (4) ◽  
pp. 281-287 ◽  
Author(s):  
B. C. Meyer ◽  
R. Raman ◽  
R. Rao ◽  
R. D. Fellman ◽  
J. Beer ◽  
...  
Keyword(s):  
Video Clip
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